1. Field of the Invention
This invention relates to computer systems and, more particularly, to storage management within computer systems.
2. Description of the Related Art
Many business organizations and governmental entities rely upon applications that access large amounts of data, often exceeding a terabyte or more of data, for mission-critical applications. A variety of different storage devices, potentially from multiple storage vendors, with varying functionality, performance and availability characteristics, may be employed in such environments. Numerous data producers (i.e., sources of new data and updates to existing data) and data consumers with different sets of storage access requirements may need to share access to the stored data. In some enterprise environments, hundreds or thousands of data producers and data consumers may be operating at any given time. Sustained update rates on the order of tens to hundreds of gigabytes per hour may need to be supported in large enterprise data centers, with spikes of even higher levels of I/O activity. In some environments, furthermore, access patterns may be skewed towards the most recently updated data: that is, instead of being uniformly spread over the entire data set, a relatively large proportion of write and read requests may be directed at a “working set” of recently modified data.
As the heterogeneity and complexity of storage environments increases, and as the size of the data being managed within such environments increases, providing a consistent quality of service for storage operations may become a challenge. Quality of service requirements may include the ability to predictably sustain performance levels (e.g., I/O throughput for applications such as database management), data integrity requirements, and the ability to recover rapidly from application, host and/or device failures. At the same time, advanced storage features, such as temporal storage management (i.e., the ability to view and/or update data as of specified points in time), replication, and archival capabilities, may also be a requirement for enterprise-level storage environments.
Given the high data production rates described above, efficient update and search mechanisms, as well as appropriate capacity planning or sizing, may be essential for ensuring predictable performance. In a typical production environment supporting applications such as database management servers and file systems, servers providing access to the storage devices may be sized and/or configured based on an a model that may incorporate expected I/O workloads (e.g., the typical number of concurrent I/O clients, typical ratios of reads to writes, etc.) for the applications. However, in addition to normal read and write operations performed on behalf of such applications, advanced storage features, such as storage archival, replication, frozen image or point-in-time versioning, and backup, may also impact the load experienced by the production servers. Such advanced storage features may be hard to include within sizing estimates for storage servers for a variety of reasons, for example because the resources required to replicate a set of volumes may increase over time (e.g., as the amount of source data for the replication grows) and may vary with the characteristics of the replication destination (e.g., the speed with which data may be transmitted to and copied at the destination, which may in turn depend on the physical location of the destination devices). It may therefore be desirable to separate production workloads from advanced storage feature workload, to minimize the impact of implementing advanced storage features upon production server performance.
In addition to the performance problems outlined above, the potential for data corruption may also increase with the size and complexity of an enterprise storage environment. While numerous vendors attempt to provide solutions to the well-known external sources of data corruption, such as malicious intruders, worms, viruses etc., the very complexity of managing large collections of storage devices may also increase the probability of inadvertent data corruption due to internal sources in some cases. One source of inadvertent data corruption may arise due to multiple users being authorized to write to a given production data storage device for different purposes. For example, a database administrator may be in charge of expanding the underlying storage space used by a database management system (e.g., by enlarging a logical volume or adding more logical volumes), and a file system administrator may be in charge of creating a clone or a replica of a file system. If the storage devices being targeted by the two administrators for their respective operations happen to overlap (e.g., if a portion of the same disk is used by both), data corruption may result. Both administrators may have been granted high levels of authorization (e.g., “root” access in Unix-like systems), allowing one to overwrite the data of the other, and to potentially damage production data. In order to reduce the chances of data corruption from both external and internal sources, and to more easily identify a cause in the event data corruption does occur, it may be useful to place restrictions on the ability to modify production data, for example by allowing production data to be modified directly only from a small set of trusted processes or hosts.
The requirements for high sustained performance, stability and predictability, advanced features such as temporal data management, replication, archival, and frozen image services, combined with the need for improved data integrity, may place a high burden on storage system managers. Mechanisms and services that support high throughputs for updates and efficient data sharing, while at the same time decoupling production systems from advanced storage feature workload, and limiting the ability to corrupt production data, may therefore be highly desirable in enterprise-level storage environments.